Latent Similarity Identifies Important Functional Connections for Phenotype Prediction.

OBJECTIVE: Endophenotypes such as brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often encounter limited numbers of subjects but high dimensional imaging features, hindering reproducibility. Therefore, we develop an interpretable, multivariate classification/regression algorithm, called Latent Similarity (LatSim), suitable for small sample size but high feature dimension datasets. METHODS: LatSim combines metric learning with a kernel similarity function and softmax aggregation to identify task-related similarities between subjects. Inter-subject similarity is utilized to improve performance on three prediction tasks using multi-paradigm fMRI data. A greedy selection algorithm, made possible by LatSim's computational efficiency, is developed as an interpretability method. RESULTS: LatSim achieved significantly higher predictive accuracy at small sample sizes on the Philadelphia Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave superior discriminative power compared to those identified by other methods. We identified 4 functional brain networks enriched in connections for predicting brain age, sex, and intelligence. CONCLUSION: We find that most information for a predictive task comes from only a few (1-5) connections. Additionally, we find that the default mode network is over-represented in the top connections of all predictive tasks. SIGNIFICANCE: We propose a novel prediction algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data. Our work can lead to new insights in both algorithm design and neuroscience research.

Homogenizing Estimates of Heritability Among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data.

Imaging genetic analyses use heritability calculations to measure the fraction of phenotypic variance attributable to additive genetic factors. We tested the agreement between heritability estimates provided by four methods that are used for heritability estimates in neuroimaging traits. SOLAR-Eclipse and OpenMx use iterative maximum likelihood estimation (MLE) methods. Accelerated Permutation inference for ACE (APACE) and fast permutation heritability inference (FPHI), employ fast, non-iterative approximation-based methods. We performed this evaluation in a simulated twin-sibling pedigree and phenotypes and in diffusion tensor imaging (DTI) data from three twin-sibling cohorts, the human connectome project (HCP), netherlands twin register (NTR) and BrainSCALE projects provided as a part of the enhancing neuro imaging genetics analysis (ENIGMA) consortium. We observed that heritability estimate may differ depending on the underlying method and dataset. The heritability estimates from the two MLE approaches provided excellent agreement in both simulated and imaging data. The heritability estimates for two approximation approaches showed reduced heritability estimates in datasets with deviations from data normality. We propose a data homogenization approach (implemented in solar-eclipse; www.solar-eclipse-genetics.org) to improve the convergence of heritability estimates across different methods. The homogenization steps include consistent regression of any nuisance covariates and enforcing normality on the trait data using inverse Gaussian transformation. Under these conditions, the heritability estimates for simulated and DTI phenotypes produced converging heritability estimates regardless of the method. Thus, using these simple suggestions may help new heritability studies to provide outcomes that are comparable regardless of software package.

Reproducibility of tract-based white matter microstructural measures using the ENIGMA-DTI protocol.

BACKGROUND: In preparation for longitudinal analyses of white matter development in youths with family histories of substance use disorders (FH+) or without such histories (FH-), we examined the reproducibility and reliability of global and regional measures of fractional anisotropy (FA) values, measured using the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA)-diffusion tensor imaging (DTI) protocol. Highly reliable measures are necessary to detect any subtle differences in brain development. METHODS: First, we analyzed reproducibility data in a sample of 12 healthy young adults (ages 20-28) imaged three times within a week. Next, we calculated the same metrics in data collected 1-year apart in the sample of 68 FH+ and 21 FH- adolescents. This is a timeframe where within subject changes in white matter microstructure are small compared to between subject variance. Reproducibility was estimated by examining mean coefficients of variation (MCV), mean absolute differences (MAD), and intraclass correlations (ICC) for global and tract-specific FA values. RESULTS: We found excellent reproducibility for whole-brain DTI-FA values and most of the white matter tracts, except for the corticospinal tract and the fornix in both adults and youths. There was no significant effect of FH-group on reproducibility (p = .4). Reproducibility metrics were not significantly different between adolescents and adults (all p > .2). In post hoc analyses, the reproducibility metrics for regional FA values showed a strong positive correlation (r = .6) with the regional FA heritability measures previously reported by ENIGMA-DTI. CONCLUSION: Overall, this study demonstrated an excellent reproducibility of ENIGMA-DTI FA, positing it as viable analysis tools for longitudinal studies and other protocols that repeatedly assess white matter microstructure.

Heterochronicity of white matter development and aging explains regional patient control differences in schizophrenia.

BACKGROUND: Altered brain connectivity is implicated in the development and clinical burden of schizophrenia. Relative to matched controls, schizophrenia patients show (1) a global and regional reduction in the integrity of the brain's white matter (WM), assessed using diffusion tensor imaging (DTI) fractional anisotropy (FA), and (2) accelerated age-related decline in FA values. In the largest mega-analysis to date, we tested if differences in the trajectories of WM tract development influenced patient-control differences in FA. We also assessed if specific tracts showed exacerbated decline with aging. METHODS: Three cohorts of schizophrenia patients (total n = 177) and controls (total n = 249; age = 18-61 years) were ascertained with three 3T Siemens MRI scanners. Whole-brain and regional FA values were extracted using ENIGMA-DTI protocols. Statistics were evaluated using mega- and meta-analyses to detect effects of diagnosis and age-by-diagnosis interactions. RESULTS: In mega-analysis of whole-brain averaged FA, schizophrenia patients had lower FA (P = 10-11 ) and faster age-related decline in FA (P = 0.02) compared with controls. Tract-specific heterochronicity measures, that is, abnormal rates of adolescent maturation and aging explained approximately 50% of the regional variance effects of diagnosis and age-by-diagnosis interaction in patients. Interactive, three-dimensional visualization of the results is available at www.enigma-viewer.org. CONCLUSION: WM tracts that mature later in life appeared more sensitive to the pathophysiology of schizophrenia and were more susceptible to faster age-related decline in FA values. Hum Brain Mapp 37:4673-4688, 2016. © 2016 Wiley Periodicals, Inc.

The common genetic influence over processing speed and white matter microstructure: Evidence from the Old Order Amish and Human Connectome Projects.

Speed with which brain performs information processing influences overall cognition and is dependent on the white matter fibers. To understand genetic influences on processing speed and white matter FA, we assessed processing speed and diffusion imaging fractional anisotropy (FA) in related individuals from two populations. Discovery analyses were performed in 146 individuals from large Old Order Amish (OOA) families and findings were replicated in 485 twins and siblings of the Human Connectome Project (HCP). The heritability of processing speed was h(2)=43% and 49% (both p<0.005), while the heritability of whole brain FA was h(2)=87% and 88% (both p<0.001), in the OOA and HCP, respectively. Whole brain FA was significantly correlated with processing speed in the two cohorts. Quantitative genetic analysis demonstrated a significant degree to which common genes influenced joint variation in FA and brain processing speed. These estimates suggested common sets of genes influencing variation in both phenotypes, consistent with the idea that common genetic variations contributing to white matter may also support their associated cognitive behavior.

Heritability of complex white matter diffusion traits assessed in a population isolate.

INTRODUCTION: Diffusion weighted imaging (DWI) methods can noninvasively ascertain cerebral microstructure by examining pattern and directions of water diffusion in the brain. We calculated heritability for DWI parameters in cerebral white (WM) and gray matter (GM) to study the genetic contribution to the diffusion signals across tissue boundaries. METHODS: Using Old Order Amish (OOA) population isolate with large family pedigrees and high environmental homogeneity, we compared the heritability of measures derived from three representative DWI methods targeting the corpus callosum WM and cingulate gyrus GM: diffusion tensor imaging (DTI), the permeability-diffusivity (PD) model, and the neurite orientation dispersion and density imaging (NODDI) model. These successively more complex models represent the diffusion signal modeling using one, two, and three diffusion compartments, respectively. RESULTS: We replicated the high heritability of the DTI-based fractional anisotropy (h(2) = 0.67) and radial diffusivity (h(2) = 0.72) in WM. High heritability in both WM and GM tissues were observed for the permeability-diffusivity index from the PD model (h(2) = 0.64 and 0.84), and the neurite density from the NODDI model (h(2) = 0.70 and 0.55). The orientation dispersion index from the NODDI model was only significantly heritable in GM (h(2) = 0.68). CONCLUSION: DWI measures from multicompartmental models were significantly heritable in WM and GM. DWI can offer valuable phenotypes for genetic research; and genes thus identified may reveal mechanisms contributing to mental and neurological disorders in which diffusion imaging anomalies are consistently found. Hum Brain Mapp 37:525-535, 2016. © 2015 Wiley Periodicals, Inc.

Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data.

The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h(2)=0.53-0.90, p<10(-5)), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests that the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application.